Randomize Floyd

Randomize Floyd | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Randomize Floyd Leonardo Araujo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7906220/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Traditional Floyd-Steinberg (FS) error diffusion is a fundamental halftoning technique that yields high perceptual quality, but is hampered by two major drawbacks: the generation of visually intrusive “worm-like” artifacts and an inherently serial implementation that precludes efficient parallelization. This paper addresses both limitations by proposing and empirically validating a series of variations centered on randomization of the kernel weight coefficients. We develop an interactive simulation interface to systematically test the effects of stochastic elements, adaptive visual models, and parallel block processing. Our results, verified through visual comparison and quantitative spectral analysis (RAPSD and Anisotropy measures), demonstrate that introducing randomized weights successfully disrupts the deterministic error path, effectively suppress- ing low-frequency artifacts and driving the noise spectrum toward an ideal blue noise distribution. Randomization decouples pixel dependencies, enabling better-quality results even with parallel approaches like block-based process- ing. This work concludes that weight randomization is the key mechanism for achieving perceptually superior, artifact-free error diffusion while simultaneously facilitating the path toward modern, efficient parallel implementations. error diffusion dithering blue noise randomization parallel processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 20 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7906220","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538563100,"identity":"b8940c78-bc2a-41bf-9333-28c3f1854004","order_by":0,"name":"Leonardo 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